Real-Time Predictive Recommendation System Using Per-Set Optimization

ABSTRACT

In general, embodiments of the present invention provide systems, methods and computer readable media configured to use a per-set level optimization of the rank order of promotions to be recommended to a consumer. In some embodiments, machine learning is used offline to generate a predictive diversity model that receives one or more similarity rank features associated with a promotion (e.g., category, price band) as input, and produces an output multiplier to be applied to the promotion&#39;s respective associated relevance score (e.g., a relevance score representing a prediction of the promotion&#39;s conversion rate without diversity features). At run time, per-set optimization of the ordering of a set of promotions is implemented by adjusting the respective associated relevance scores of the promotions using the diversity model and then re-ordering the set of promotions based on their respective adjusted relevance scores.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/709,296, titled “REAL-TIME PREDICTIVE RECOMMENDATION SYSTEM USINGPER-SET OPTIMIZATION,” filed May 11, 2015, which claims the benefit ofU.S. Provisional Application No. 61/993,982, entitled “REAL-TIMEPREDICTIVE RECOMMENDATION SYSTEM USING PER-SET OPTIMIZATION,” and filedMay 15, 2014, the contents of which are hereby incorporated by referenceherein in their entirety.

FIELD

Embodiments of the invention relate, generally, to a real-timepredictive recommendation system for promotions using a per-setoptimization of promotion rankings determined by predictive modelsgenerated from relevance data sources.

BACKGROUND

Current methods for recommending promotions to consumers for purchaseexhibit a plurality of problems that make current systems insufficient,ineffective and/or the like. Through applied effort, ingenuity, andinnovation, solutions to improve such methods have been realized and aredescribed in connection with embodiments of the present invention.

SUMMARY

The capability to recommend promotions for purchase that are mostrelevant to each consumer is important for a promotion and marketingservice, because maintaining an active and engaged customer base meansmaximizing profits. In some embodiments, a relevance system is used toselect promotions to be recommended to a consumer (i.e., the availablepromotions that are most relevant to the consumer) based on using storeddata representing attributes of promotions and/or the consumer, and isperformed by executing a workflow that specifies a sequence of filteringrules and/or algorithms to be applied in selecting the relevantpromotions. In some embodiments, the promotions selected forrecommendation to consumers are ranked based on predictions of promotionperformance and consumer behavior. The top-ranked promotions may befeatured in a presentation to the consumer.

In some embodiments, each of a set of available promotions to berecommended to a particular consumer can be sorted and/or rankedaccording to a probability that the consumer's behavior in response tothe promotion will match a ranking target (e.g., conversion rate, grossrevenue). In some embodiments, promotions available to a consumer areranked based on a relevance model derived from one or more data sourcesrepresenting attributes of promotions and consumer behavior. Using themodel, each promotion is associated with a relevance score thatrepresents the probability that the consumer's behavior with respect tothe promotion will approach the ranking target while the consumerinteracts with an impression containing content describing thepromotion. In some embodiments, the set of promotions selected forrecommendation to a consumer can be sorted and/or ranked based on theirrespective associated relevance scores.

In embodiments, the relevance model may be a predictive function. Insome embodiments, the predictive function may be a trainable functionthat is developed using machine learning. In some embodiments, thepredictive function may be generated offline using supervised learningin a set of modeling stages in which the function is adapted based ontraining data sets of features that are extracted from a set of datasources (e.g., log data, promotion and user attribute data). In someembodiments, the set of data sources includes contextual data sources.In embodiments, examples of contextual data sources for mobile consumersmay include user locations (prior, current, or potential) and theirassociated categories of interest; prior promotion interest level shownby the consumer; and prior promotion subcategory interest level.

Ranking promotions based on their respective attributes may not be theonly factor to consider when ordering promotions for presentation to aconsumer. The arrangement of promotions within the context of apresentation may be a factor in determining the ordering of a set ofrelevant promotions to be recommended for a consumer. Ranking promotionsbased on individual attributes often results in clumping of most similarpromotions, so a presentation of recommended promotions based on theirrank order may show sequences of very similar types of promotions (e.g.,the featured restaurants in a returned set of recommended restaurantsmay include too many sushi restaurants and not enough of other types ofrestaurants). Thus, in embodiments, the rank ordering of promotions tobe recommended to a consumer may be optimized at the per-set level,i.e., optimized based on promotion features of a set of promotions withwhich a particular promotion is associated. In some embodiments, per-setoptimization of rank ordering is implemented using a predictive modelthat is generated by applying machine learning to map one or moreper-set features associated with a promotion to a multiplier that is apredictor of the effect of the feature values on the ranking of thepromotion.

Predictive modeling at the per set level is more challenging thanpredictive modeling at the per item level for reasons including:

-   -   analysis features are more complex because they are in general        dependent upon the arrangement of items, which grows extremely        quickly as the number of items increases (faster than        exponential)    -   training data becomes very sparse because of the large number of        potential set configurations

One example of per-set optimization of the rank ordering of recommendedpromotions is per-set optimization for diversity. It has beendemonstrated that a consumer is more likely to remain active and engagedif there is more diversity in recommendation results (e.g., in referenceto the restaurant example, adding a relevant restaurant from anothercategory before or after sushi restaurants to spice things up).

At a high level, the diversity of a set of promotions will be theperceived diversity as the consumer scrolls through the results. At anypoint in time during the consumer session, the perceived diversity willbe greater if the current promotion being viewed and the neighboringpromotions are as dissimilar as possible, e.g., if the current promotionis a restaurant promotion, then the preceding and following promotionsshould be different, e.g., leisure activity preceding and healthfollowing.

As such, and according to some example embodiments, the systems andmethods described herein are therefore configured to use a per-set leveloptimization of the rank order of promotions to be recommended to aconsumer. In some embodiments, machine learning is used offline togenerate a predictive diversity model that receives one or moresimilarity rank features associated with a promotion (e.g., category,price band) as input, and produces an output multiplier to be applied tothe promotion's respective associated relevance score (e.g., a relevancescore representing a prediction of the promotion's conversion ratewithout diversity features). At run time, per-set optimization of theordering of a set of promotions is implemented by adjusting therespective associated relevance scores of the promotions using thediversity model and then re-ordering the set of promotions based ontheir respective adjusted relevance scores.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates an example system that can be configured to implementthe relevance ranking of promotions that are available from a promotionand marketing service and are to be recommended to a particular consumerin accordance with some embodiments discussed herein;

FIG. 2 is a flow diagram of an example method for per-set optimizationof the rank order of a set of promotions to be recommended to a consumerin order to increase the diversity of the presentation of the promotionsin accordance with some embodiments discussed herein;

FIG. 3 is a chart illustrating the effect of per-set feature data onestimated conversion rate in accordance with some embodiments discussedherein; and

FIG. 4 illustrates a schematic block diagram of circuitry that can beincluded in a computing device, such as a recommendation engine, inaccordance with some embodiments discussed herein.

DETAILED DESCRIPTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the invention are shown. Indeed, this invention may beembodied in many different forms and should not be construed as beinglimited to the embodiments set forth herein; rather, these embodimentsare provided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

As described herein, system components can be communicatively coupled toone or more of each other. Though the components are described as beingseparate or distinct, two or more of the components may be combined intoa single process or routine. The component functional descriptionsprovided herein including separation of responsibility for distinctfunctions is by way of example. Other groupings or other divisions offunctional responsibilities can be made as necessary or in accordancewith design preferences.

As used herein, the terms “data,” “content,” “information” and similarterms may be used interchangeably to refer to data capable of beingcaptured, transmitted, received, displayed and/or stored in accordancewith various example embodiments. Thus, use of any such terms should notbe taken to limit the spirit and scope of the disclosure. Further, wherea computing device is described herein to receive data from anothercomputing device, the data may be received directly from the anothercomputing device or may be received indirectly via one or moreintermediary computing devices, such as, for example, one or moreservers, relays, routers, network access points, base stations, and/orthe like. Similarly, where a computing device is described herein tosend data to another computing device, the data may be sent directly tothe another computing device or may be sent indirectly via one or moreintermediary computing devices, such as, for example, one or moreservers, relays, routers, network access points, base stations, and/orthe like.

As used herein, the term “promotion and marketing service” may refer,without limitation, to a service that is accessible via one or morecomputing devices and is operable to provide example promotion and/ormarketing services on behalf of one or more providers that are offeringone or more instruments that are redeemable for goods, services,experiences and/or the like. The promotion and marketing service isfurther configured to illustrate or otherwise inform one or moreconsumers of the availability of one or more instruments in the form ofone or more impressions. In some examples, the promotion and marketingservice may also take the form of a redemption authority, a paymentprocessor, a rewards provider, an entity in a financial network, apromoter, an agent and/or the like. As such, the service is, in someexample embodiments, configured to present one or more promotions viaone or more impressions, accept payments for promotions from consumers,issue instruments upon acceptance of an offer, participate inredemption, generate rewards, provide a point of sale device or service,issue payments to providers and/or or otherwise participate in theexchange of goods, services or experiences for currency, value and/orthe like.

As used herein, the term “provider” may be used to refer, withoutlimitation, to a merchant, business owner, consigner, shopkeeper,tradesperson, vender, operator, entrepreneur, agent, dealer,organization or the like that is in the business of a providing a good,service or experience to a consumer, facilitating the provision of agood, service or experience to a consumer and/or otherwise operating inthe stream of commerce. For example, a provider may be in the form of arunning company that sells attire that is generally used by a person whoruns or participates in athletic activities.

As used herein, the terms “promotion,” “offer,” “deal” and similar termsmay be used interchangeably to refer, without limitation, to any type ofoffered, presented or otherwise indicated reward, discount, coupon,credit, incentive, discount, media or the like that is indicative of apromotional value or the like that upon purchase or acceptance resultsin the issuance of an instrument that may be used toward at least aportion of the purchase of particular goods, services and/or experiencesdefined by the promotion. An example promotion, using the aforementionedrunning company as the example provider, is $25 for $50 toward runningshoes. In some examples, the promotion defines an accepted value (e.g.,a cost to purchase the promotion), a promotional value (e.g., the valueof the resultant instrument beyond the accepted value), a residual value(e.g., the value upon return or upon expiry of one or more redemptionparameters), one or more redemptions parameters and/or the like. Forexample, and using the running company promotion as an example, theaccepted value is $25 and the promotional value is $50. In this example,the residual value may be equal to the accepted value.

As used herein, the term “instrument” may be used, without limitation,to refer to any type of gift card, tender, electronic certificate,medium of exchange, voucher, or the like that embodies the terms of thepromotion from which the instrument resulted and may be used toward atleast a portion of the purchase, acquisition, procurement, consumptionor the like of goods, services and/or experiences. In some examples, theinstrument may take the form of tender that has a given value that isexchangeable for goods, services and/or experiences and/or a reductionin a purchase price of a particular good, service or experience. In someexamples, the instrument may have multiple values, such as acceptedvalue, a promotional value and/or a residual value. For example, usingthe aforementioned running company as the example provider, anelectronic indication in a mobile application that shows $50 of value tospend at the running company. In some examples, the accepted value ofthe instrument is defined by the value exchanged for the instrument. Insome examples, the promotional value is defined by the promotion fromwhich the instrument resulted and is the value of the instrument beyondthe accepted value. In some examples, the residual value is the valueafter redemption, the value after the expiry or other violation of aredemption parameter, the return or exchange value of the instrumentand/or the like.

As used herein, the term “impression” may be used, without limitation,to refer to a communication, a display, or other perceived indication,such as a flyer, print media, e-mail, text message, application alert,mobile applications, other type of electronic interface or distributionchannel and/or the like, of one or more promotions. For example, andusing the aforementioned running company as the example provider, ane-mail communication sent to consumers that indicates the availabilityof a $25 for $50 toward running shoes promotion.

As used herein, the terms “consumer” and “customer” may be usedinterchangeably to refer, without limitation, to a client, customer,purchaser, shopper, user or the like who may be in the position to ordoes exchange value for one or more instruments under the terms definedby the one or promotions. For example, and using the aforementionedrunning company as the example provider, an individual who is interestedin purchasing running shoes.

FIG. 1 illustrates an example system 100 that can be configured toimplement the relevance ranking of promotions that are available from apromotion and marketing service and are to be recommended to aparticular consumer (i.e., “user”). System 100 comprises a relevanceservice 110 that returns a set of available promotions 112 that areranked for relevance to a consumer in response to receiving a request102 for available promotions on behalf of the consumer; a user profilesrepository 120 in which data representing profile attributes ofconsumers are stored; a promotions repository 130 in which datarepresenting attributes of promotions are stored; a user activationstates repository 140 in which data representing consumer activationstates respectively associated with consumers are stored; and a userbehavioral data repository 150 in which historical data representing therespective consumer behavior of consumers are stored. The relevanceservice 110 includes a recommendation engine 115 that is configured toselect available promotions to be recommended to a particular consumer.

In some embodiments, recommendation engine 115 generates a set ofavailable promotions that are most relevant to a consumer in response toreceiving consumer identification data representing the consumer. Insome embodiments, generating the set of available promotions includesselecting the promotions using stored data representing attributes ofpromotions and/or the consumer, and is performed by executing a workflowthat specifies a sequence of filtering rules and/or algorithms to beapplied in selecting the relevant promotions. A workflow may includeranking the selected promotions for relevance to the consumer and thenordering the selected promotions based on their respective rankings. Insome embodiments, the most highly ranked promotions may be presented tothe consumer as featured recommended promotions. In some embodiments,recommendation engine 115 may be configured to include per-setoptimization of the ordering of the selected promotions.

FIG. 2 is a flow diagram of an example method 200 for per-setoptimization of the rank order of a set of promotions to be recommendedto a consumer in order to increase the diversity of the presentation ofthe promotions. For convenience, the method 200 will be described withrespect to a system, including one or more computing devices, thatperforms the method 200. Specifically, the method 200 will be describedwith respect to its implementation by recommendation engine 115 insystem 100.

In embodiments, the system receives 205 a set of promotions to bepresented to a consumer in an impression. In some embodiments, the setof promotions is ordered based on their respective base relevancescores, each of which has been calculated based on the output of apredictive relevance model.

In some embodiments, the predictive relevance model can be generatedoffline using supervised learning. In a typical supervised learningscenario, a predictive function that maps an input value to one of a setof predefined output values is adapted, in response to exposure to atraining data set containing examples of inputs and their respectiveassociated outputs, to perform a mapping that represents a particularpredictive model. In some embodiments, the predictive function maps datarepresenting the promotion's performance and the consumer's behavior toone of a set of probability classes, each class representing a differentprobability that the data matches a ranking target.

In some embodiments, the relevance score associated with a promotion iscalculated based on a predictive model for which the inputs are featuresthat may represent attributes of the promotion and/or attributes of theconsumer, and for which the ranking target is consumer conversion rate.In this case, the base relevance score calculated for a particularpromotion using the predictive relevance model represents an estimatedconversion rate per impression for the promotion.

In embodiments, the system calculates 210 a diversity multiplier foreach of the set of promotions. In some embodiments, the diversitymultiplier is calculated using a predictive diversity model that maps afeature set including one or more per-set features associated with apromotion to a diversity multiplier output representing the predictedeffect of the feature set on the promotion's base relevance score (e.g.,the effect of the diversity multiplier on the estimated conversion ratefor the promotion).

In some embodiments, the predictive diversity model is generated offlineusing supervised learning. The selection of predictive model to generateis not critical to the invention; examples of predictive models includetrainable functions (e.g., trainable classifiers), neural networks, andensembles of trees. In embodiments, the per-set feature set may bechosen based on a statistical analysis to determine the mostdiscriminant features to represent the modeling task. Examples ofper-set features that may be used as input for a predictive diversitymodel include promotion category, price band, and location.

In some embodiments, the training data set from which the predictivediversity model is derived is generated from the same historical datafrom which the predictive relevance model is derived. The historicaldata represent consumer behavior and promotion performance collectedfrom impressions previously presented to consumers. In embodiments inwhich the base relevance score represents estimated conversion rate,each training data set instance (representing a previously presentedpromotion) includes per-set input to the model generated by extractingper-set feature data describing the promotion and the target diversitymultiplier output from that input, which is calculated by taking theratio of the predicted base score for the promotion (estimatedconversion rate calculated using the predictive relevance model) to theactual conversion rate that occurred. An example of historical datarepresenting the effect of per-set feature data on estimated conversionrate will be discussed in more detail below with reference to chart 300in FIGS. 3.

In embodiments, the system adjusts 215 the base relevance score for eachpromotion using the promotion's respective diversity multiplier.

In embodiments, the system re-orders 220 the set of promotions based ontheir respective adjusted relevance scores. In some embodiments, thediversity of a set of recommended promotions is increased because there-ordering based on the adjusted scores creates gaps in the clumps ofsimilar promotions from the original ordering, and the gaps are filledby more highly-ranked dissimilar promotions.

FIG. 3 is a chart 300 illustrating the effect of per-set feature data onestimated conversion rate. The chart 300, based on historical datacollected from promotions previously presented to consumers inimpressions, depicts the ratio of the actual conversion rate over thepredicted conversion rate as a function of the rank of a promotionwithin its category. If a promotion is the first to appear in itscategory, it has a 30% greater chance of being purchased than if it wereto appear after 4 other promotions in the same category. Conversely, ifthe promotion appears after the consumer already has seen 16 promotionsfrom the same category, it has a 50% lower chance of being purchased ascompared to being the first to be seen. Similar effects are seen whencomparing promotions in the same price band.

FIG. 4 depicts a schematic block diagram of circuitry 400, some or allof which may be included in, for example, recommendation engine 115. Asillustrated in FIG. 4, in accordance with some example embodiments,circuitry 400 can include various means, such as processor 402, memory404, communications module 406, and/or input/output module 408. Asreferred to herein, “module” includes hardware, software and/or firmwareconfigured to perform one or more particular functions. In this regard,the means of circuitry 400 as described herein may be embodied as, forexample, circuitry, hardware elements (e.g., a suitably programmedprocessor, combinational logic circuit, and/or the like), a computerprogram product comprising computer-readable program instructions storedon a non-transitory computer-readable medium (e.g., memory 404) that isexecutable by a suitably configured processing device (e.g., processor402), or some combination thereof.

Processor 402 may, for example, be embodied as various means includingone or more microprocessors with accompanying digital signalprocessor(s), one or more processor(s) without an accompanying digitalsignal processor, one or more coprocessors, one or more multi-coreprocessors, one or more controllers, processing circuitry, one or morecomputers, various other processing elements including integratedcircuits such as, for example, an ASIC (application specific integratedcircuit) or FPGA (field programmable gate array), or some combinationthereof. Accordingly, although illustrated in FIG. 4 as a singleprocessor, in some embodiments, processor 402 comprises a plurality ofprocessors. The plurality of processors may be embodied on a singlecomputing device or may be distributed across a plurality of computingdevices collectively configured to function as circuitry 400. Theplurality of processors may be in operative communication with eachother and may be collectively configured to perform one or morefunctionalities of circuitry 400 as described herein. In an exampleembodiment, processor 402 is configured to execute instructions storedin memory 404 or otherwise accessible to processor 402. Theseinstructions, when executed by processor 402, may cause circuitry 400 toperform one or more of the functionalities of circuitry 400 as describedherein.

Whether configured by hardware, firmware/software methods, or by acombination thereof, processor 402 may comprise an entity capable ofperforming operations according to embodiments of the present inventionwhile configured accordingly. Thus, for example, when processor 402 isembodied as an ASIC, FPGA or the like, processor 402 may comprisespecifically configured hardware for conducting one or more operationsdescribed herein. Alternatively, as another example, when processor 402is embodied as an executor of instructions, such as may be stored inmemory 404, the instructions may specifically configure processor 402 toperform one or more algorithms and operations described herein, such asthose discussed in connection with FIG. 1.

Memory 404 may comprise, for example, volatile memory, non-volatilememory, or some combination thereof. Although illustrated in FIG. 4 as asingle memory, memory 404 may comprise a plurality of memory components.The plurality of memory components may be embodied on a single computingdevice or distributed across a plurality of computing devices. Invarious embodiments, memory 404 may comprise, for example, a hard disk,random access memory, cache memory, flash memory, a compact disc readonly memory (CD-ROM), digital versatile disc read only memory (DVD-ROM),an optical disc, circuitry configured to store information, or somecombination thereof. Memory 404 may be configured to store information,data (including analytics data), applications, instructions, or the likefor enabling circuitry 400 to carry out various functions in accordancewith example embodiments of the present invention. For example, in atleast some embodiments, memory 404 is configured to buffer input datafor processing by processor 402. Additionally or alternatively, in atleast some embodiments, memory 404 is configured to store programinstructions for execution by processor 402. Memory 404 may storeinformation in the form of static and/or dynamic information. Thisstored information may be stored and/or used by circuitry 400 during thecourse of performing its functionalities.

Communications module 406 may be embodied as any device or meansembodied in circuitry, hardware, a computer program product comprisingcomputer readable program instructions stored on a computer readablemedium (e.g., memory 404) and executed by a processing device (e.g.,processor 402), or a combination thereof that is configured to receiveand/or transmit data from/to another device, such as, for example, asecond circuitry 400 and/or the like. In some embodiments,communications module 406 (like other components discussed herein) canbe at least partially embodied as or otherwise controlled by processor402. In this regard, communications module 406 may be in communicationwith processor 402, such as via a bus. Communications module 406 mayinclude, for example, an antenna, a transmitter, a receiver, atransceiver, network interface card and/or supporting hardware and/orfirmware/software for enabling communications with another computingdevice. Communications module 406 may be configured to receive and/ortransmit any data that may be stored by memory 404 using any protocolthat may be used for communications between computing devices.Communications module 406 may additionally or alternatively be incommunication with the memory 404, input/output module 408 and/or anyother component of circuitry 400, such as via a bus.

Input/output module 408 may be in communication with processor 402 toreceive an indication of a user input and/or to provide an audible,visual, mechanical, or other output to a user. Some example visualoutputs that may be provided to a user by circuitry 400 are discussed inconnection with FIG. 1. As such, input/output module 408 may includesupport, for example, for a keyboard, a mouse, a joystick, a display, atouch screen display, a microphone, a speaker, a RFID reader, barcodereader, biometric scanner, and/or other input/output mechanisms. Inembodiments wherein circuitry 400 is embodied as a server or database,aspects of input/output module 408 may be reduced as compared toembodiments where circuitry 400 is implemented as an end-user machine orother type of device designed for complex user interactions. In someembodiments (like other components discussed herein), input/outputmodule 408 may even be eliminated from circuitry 400. Alternatively,such as in embodiments wherein circuitry 400 is embodied as a server ordatabase, at least some aspects of input/output module 408 may beembodied on an apparatus used by a user that is in communication withcircuitry 400. Input/output module 408 may be in communication with thememory 404, communications module 406, and/or any other component(s),such as via a bus. Although more than one input/output module and/orother component can be included in circuitry 400, only one is shown inFIG. 4 to avoid overcomplicating the drawing (like the other componentsdiscussed herein).

Predictive recommendation engine module 410 may also or instead beincluded and configured to perform the functionality discussed hereinrelated to the recommendation engine discussed above. In someembodiments, some or all of the functionality of predictiverecommendation engine may be performed by processor 402. In this regard,the example processes and algorithms discussed herein can be performedby at least one processor 402 and/or predictive recommendation enginemodule 410. For example, non-transitory computer readable media can beconfigured to store firmware, one or more application programs, and/orother software, which include instructions and other computer-readableprogram code portions that can be executed to control each processor(e.g., processor 402 and/or predictive recommendation engine module 410)of the components of system 100 to implement various operations,including the examples shown above. As such, a series ofcomputer-readable program code portions are embodied in one or morecomputer program products and can be used, with a computing device,server, and/or other programmable apparatus, to producemachine-implemented processes.

As described above in this disclosure, aspects of embodiments of thepresent invention may be configured as methods, mobile devices, backendnetwork devices, and the like. Accordingly, embodiments may comprisevarious means including entirely of hardware or any combination ofsoftware and hardware. Furthermore, embodiments may take the form of acomputer program product on at least one non-transitorycomputer-readable storage medium having computer-readable programinstructions (e.g., computer software) embodied in the storage medium.Any suitable computer-readable storage medium may be utilized includingnon-transitory hard disks, CD-ROMs, flash memory, optical storagedevices, or magnetic storage devices.

Embodiments of the present invention have been described above withreference to block diagrams and flowchart illustrations of methods,apparatuses, systems and computer program products. It will beunderstood that each block of the circuit diagrams and process flowdiagrams, and combinations of blocks in the circuit diagrams and processflowcharts, respectively, can be implemented by various means includingcomputer program instructions. These computer program instructions maybe loaded onto a general purpose computer, special purpose computer, orother programmable data processing apparatus, such as processor 402and/or predictive recommendation engine module 410 discussed above withreference to FIG. 4, to produce a machine, such that the computerprogram product includes the instructions which execute on the computeror other programmable data processing apparatus create a means forimplementing the functions specified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable storage device (e.g., memory 404) that can direct acomputer or other programmable data processing apparatus to function ina particular manner, such that the instructions stored in thecomputer-readable storage device produce an article of manufactureincluding computer-readable instructions for implementing the functiondiscussed herein. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions discussed herein.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the circuit diagrams and processflowcharts, and combinations of blocks in the circuit diagrams andprocess flowcharts, can be implemented by special purpose hardware-basedcomputer systems that perform the specified functions or steps, orcombinations of special purpose hardware and computer instructions

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1-22. (canceled)
 23. A computer-implemented method for per-setoptimization of a rank order of a set of promotions to be recommended toa consumer, the method comprising: calculating a diversity multiplierfor a promotion in an ordered set of promotions based on a predictivediversity model and a diversity feature set associated with the orderedset of promotions, wherein the diversity multiplier represents an effectof the diversity feature set on likelihood of conversion of thepromotion based on one or more attributes associated with the promotion;calculating a relevance score for the promotion based on the diversitymultiplier; re-ordering the ordered set of promotions based on therelevance score for the promotion to generate a reordered set ofpromotions; and transmitting at least a portion of the reordered set ofpromotions to a consumer device associated with the consumer.
 24. Thecomputer-implemented method of claim 23, wherein the calculating thediversity multiplier is based on at least one per-set promotion featureassociated with the diversity feature set.
 25. The computer-implementedmethod of claim 23, wherein the calculating the diversity multiplier isbased on a dissimilarity between the promotion displayed on anelectronic interface and one or more other promotions displayed on theelectronic interface.
 26. The computer-implemented method of claim 23,wherein the calculating the diversity multiplier is based on a predictedeffect of the diversity feature set on an assigned base relevance scoreof the promotion.
 27. The computer-implemented method of claim 23,wherein the calculating the diversity multiplier is based on at leastone of a promotion category associated with the promotion, a price bandassociated with the promotion, and a location associated with thepromotion.
 28. The computer-implemented method of claim 23, wherein thetransmitting comprises transmitting a top N most highly rankedpromotions from the reordered set of promotions based on respectiverelevance scores.
 29. The computer-implemented method of claim 23,wherein the computer-implemented method further comprises: generatingthe predictive diversity model based on a supervised learning process.30. The computer-implemented method of claim 23, wherein thecomputer-implemented method further comprises: training the predictivediversity model based on historical data associated with a predictiverelevance model that facilitates ordering of the ordered set ofpromotions.
 31. A computer program product, stored on a computerreadable medium, comprising instructions that when executed on one ormore computers cause the one or more computers to perform operationsimplementing cross-sell promotions ranking, the operations comprising:calculating a diversity multiplier for a promotion in an ordered set ofpromotions based on a predictive diversity model and a diversity featureset associated with the ordered set of promotions, wherein the diversitymultiplier represents an effect of the diversity feature set onlikelihood of conversion of the promotion based on one or moreattributes associated with the promotion; calculating a relevance scorefor the promotion based on the diversity multiplier; re-ordering theordered set of promotions based on the relevance score for the promotionto generate a reordered set of promotions; and transmitting at least aportion of the reordered set of promotions to a consumer deviceassociated with a consumer.
 32. The computer program product of claim31, wherein the calculating the diversity multiplier is based on atleast one per-set promotion feature associated with the diversityfeature set.
 33. The computer program product of claim 31, wherein thecalculating the diversity multiplier is based on a dissimilarity betweenthe promotion displayed on an electronic interface and one or more otherpromotions displayed on the electronic interface.
 34. The computerprogram product of claim 31, wherein the calculating the diversitymultiplier is based on a predicted effect of the diversity feature seton an assigned base relevance score of the promotion.
 35. The computerprogram product of claim 31, wherein the calculating the diversitymultiplier is based on at least one of a promotion category associatedwith the promotion, a price band associated with the promotion, and alocation associated with the promotion.
 36. The computer program productof claim 31, wherein the transmitting comprises transmitting a top Nmost highly ranked promotions from the reordered set of promotions basedon respective relevance scores.
 37. The computer program product ofclaim 31, wherein the operations further comprise: generating thepredictive diversity model based on a supervised learning process. 38.The computer program product of claim 31, wherein the operations furthercomprise: training the predictive diversity model based on historicaldata associated with a predictive relevance model that facilitatesordering of the ordered set of promotions.
 39. A system, comprising: oneor more computers and one or more storage devices storing instructionsthat are operable, when executed by the one or more computers, to causethe one or more computers to perform operations implementing per-setoptimization of a rank order of a set of promotions to be recommended toa consumer, the operations comprising: calculating a diversitymultiplier for a promotion in an ordered set of promotions based on apredictive diversity model and a diversity feature set associated withthe ordered set of promotions, wherein the diversity multiplierrepresents an effect of the diversity feature set on likelihood ofconversion of the promotion based on one or more attributes associatedwith the promotion; calculating a relevance score for the promotionbased on the diversity multiplier; re-ordering the ordered set ofpromotions based on the relevance score for the promotion to generate areordered set of promotions; and transmitting at least a portion of thereordered set of promotions to a consumer device associated with aconsumer.
 40. The system of claim 39, wherein the calculating thediversity multiplier is based on a dissimilarity between the promotiondisplayed on an electronic interface and one or more other promotionsdisplayed on the electronic interface.
 41. The system of claim 39,wherein the calculating the diversity multiplier is based on a predictedeffect of the diversity feature set on an assigned base relevance scoreof the promotion.
 42. The system of claim 39, wherein the calculatingthe diversity multiplier is based on at least one of a promotioncategory associated with the promotion, a price band associated with thepromotion, and a location associated with the promotion.